ÁñÁ«³ÉÈËappÏÂÔØ

Global Navigation Satellite System (GNSS)-equipped buoys have a fundamental role in the validation of satellite altimetry. Requirements to validate next generation altimeter missions are demanding and call for a greater understanding of the systematic errors associated with the buoy approach. In this paper, we assess the present-day buoy precision using archived data from the Bass Strait validation facility. We explore potential improvements in buoy precision by addressing two previously ignored issues: changes to buoyancy as a function of external forcing, and biases induced by platform dynamics. Our results indicate the precision of our buoy against in situ mooring data is ~15 mm, with a ~8.5 mm systematic noise floor. Investigation into the tether tension effect on buoyancy showed strong correlation between currents, wind stress and buoy-against-mooring residuals. Our initial empirical correction achieved a reduction of 5 mm in the standard deviation of the residuals, with a 51% decrease in variance over low frequency bands. Corrections associated with platform orientation from an Inertial Navigation System (INS) unit showed centimetre-level magnitude and are expected to be higher under rougher sea states. Finally, we conclude with further possible improvements to meet validation requirements for the future SurfaceWater Ocean Topography (SWOT) mission.

The analysis of the correlations between the noise in different components of GPS stations has positive significance to those trying to obtain more accurate uncertainty of velocity with respect to station motion. Previous research into noise in GPS position time series focused mainly on single component evaluation, which affects the acquisition of precise station positions, the velocity field, and its uncertainty. In this study, before and after removing the common-mode error (CME), we performed one-dimensional linear regression analysis of the noise amplitude vectors in different components of 126 GPS stations with a combination of white noise, flicker noise, and random walking noise in Southern California. The results show that, on the one hand, there are above-moderate degrees of correlation between the white noise amplitude vectors in all components of the stations before and after removal of the CME, while the correlations between flicker noise amplitude vectors in horizontal and vertical components are enhanced from un-correlated to moderately correlated by removing the CME. On the other hand, the significance tests show that, all of the obtained linear regression equations, which represent a unique function of the noise amplitude in any two components, are of practical value after removing the CME. According to the noise amplitude estimates in two components and the linear regression equations, more accurate noise amplitudes can be acquired in the two components.

Common mode component (CMC) commonly exists in GPS coordinate time series, and its sources are still under investigation. It is challenging to quantify sources of CMC. This paper explores the sources of CMC and geophysical effects, including environmental loading and thermal expansion, on the Crustal Movement Observation Network of China (CMONOC) GPS coordinate time series. The CMCs of CMONOC time series are calculated using a correlation weighted stacking filtering method both before and after applying environmental loading and thermal expansion corrections, and the spectral characteristics of the CMC and the effects of these two geophysical factors on the CMC of CMONOC coordinate time series are then analyzed. The results indicate that environmental loading mainly affects the vertical component of the CMC, and it is not significant for the horizontal components. The reduction of the root mean square (RMS) of the CMC can be up to 1.5 mm in the vertical component and is less than 0.2 mm in the horizontal components. However, the effect of thermal expansion is not significant. The average RMS variation caused by thermal expansion correction of the CMC is less than 0.1 mm. The correlation coefficients are used as indicators of the CMC variation. The results show that the vertical coefficients generally decreased by an average of 28.8% after applying environmental loading correction, which indicates that the CMC is reduced by the environmental loading correction. In conclusion, approximately 20% reduction of the RMS of CMC by the environmental loading correction indicates that environmental loading is one of the main potential sources of the CMC in the vertical component and that thermal expansion may not be a major source of CMC. The results help explain the sources of CMC and can be used to improve the signal-to-noise ratio of GPS data.